In mixed-traffic environments where autonomous and human-driven vehicles may co-exist, motion planning for autonomous vehicles requires anticipating the future behaviors of surrounding human drivers. Existing reinforcement learning-based methods generally directly incorporate the predicted human intents into the observation to enable a proactive planning. However, human intent is inherently uncertain due to the behavioral diversity, perception noise, and partial observability. Treating predicted intends as deterministic states can result in unsafe decisions for autonomous vehicles. To address this problem, we propose Uncertainty-Aware Motion Planning (UAMP), which incorporates uncertainty in human intent prediction for AV decision-making. Specifically, UAMP first introduces a proximity-aware uncertainty estimator to quantify the interaction-conditioned intent uncertainty and constructs an uncertainty-guided joint intent distribution over surrounding human-driven vehicles. Within this uncertainty set, UAMP further introduces Uncertainty-Calibrated Value Learning (UCVL) to correct value function learning biases arising from directly incorporating uncertain human intent predictions into the observation. Extensive experiments in various mixed-traffic scenarios show that UAMP significantly improves safety and driving comfort, while maintaining traffic efficiency compared with existing approaches. The code is released at https://anonymous.4open.science/r/UAMP-5638.

翻译：在自动驾驶车辆与人类驾驶车辆可能共存的混合交通环境中，自动驾驶车辆的运动规划需要预判周围人类驾驶员的未来行为。现有基于强化学习的方法通常直接将预测的人类意图融入观测状态以实现前瞻性规划。然而，由于行为多样性、感知噪声及部分可观测性，人类意图本身具有内在不确定性。将预测意图视为确定性状态可能导致自动驾驶车辆做出不安全决策。针对该问题，我们提出不确定性感知运动规划（UAMP），该方法在自动驾驶决策中引入人类意图预测的不确定性。具体而言，UAMP首先引入邻近感知不确定性估计器，量化交互条件驱动的意图不确定性，并构建面向周围人类驾驶车辆的不确定性引导联合意图分布。在此不确定性集合中，UAMP进一步引入不确定性校准值学习（UCVL），以修正因直接将不确定的人类意图预测融入观测状态而引发的值函数学习偏差。在多种混合交通场景下的广泛实验表明，与现有方法相比，UAMP在保持交通效率的同时显著提升了安全性与驾驶舒适性。相关代码已开源至https://anonymous.4open.science/r/UAMP-5638。